For example, a cross-section image (also called a tomographic image) including internal organs is outputted as grayscale brightness information from a medical imaging apparatus such as a Computed Tomography (CT), Magnetic Resonance Imaging (MRI) apparatus, ultrasonic echo apparatus or the like. A technique is currently developed to extract a region of a designated organ from this brightness information to utilize information such as an organ's shape and a diameter of a blood vessel for the diagnosis. A medical diagnosis apparatus (e.g. CT or MRI apparatus) or ultrasonic measurement apparatus outputs plural images obtained by slicing the three-dimensional space. Images for the diagnosis are generated by carrying out an image processing for these images to extract the size of the organ to be examined or the length of a disease portion, and are utilized for the diagnosis. On the other hand, organ shapes such as blood vessel shape and the like are generated from such processing results, and the generated organ shapes are utilized as inputs for the numerical simulation to track the bloodstream or the like.
Such image processing techniques also include a region extraction technique. The region extraction technique includes various methods such as a method using a threshold, a method using spacial inclination of the brightness values, a method for dynamically securing a region based on the brightness value at a starting point on the image (i.e. Region Growing method), a method for carrying out transformation so that a designated closed segment encompasses a region to be extracted (i.e. Active Contour method), and Level Set method. These methods are implemented in medical visualization programs.
In any methods, regions are obtained to output temporal changes of boundaries of such regions or the volumes in such regions. The doctor conducts the diagnosis using this data. Or, the doctor utilizes the obtained boundaries as input shapes of the numerical simulation. As for the aforementioned conventional arts, the improvement on the accuracy of the region extraction advances.
Moreover, in such conventional arts, because of artifacts due to various factors occurred when photographed or unevenness of contrast media fulfilled into the blood vessel, there is a case where it is difficult to obtain a desired region as one region. In addition, in order to obtain plural objects such as the myocardium of the heart, fluid region or the like, the following processing is carried out for the same data plural times. More specifically, a threshold is set for each region, it is determined whether or not the regions can be finely obtained, and when they cannot be finely obtained, the threshold is reset. Such a procedure is repeated for each object. Accordingly, a lot of works needs. In addition, even when the regions are extracted, one brightness value is not set for one region, and there are a lot of noises.
In addition, another technique exists to automatically distinguish nidus candidates from medical images. In this technique, the medical image is multivalued, center coordinates of the shadow are calculated based on the multivalued image, and the nidus candidates are extracted by carrying out various image processing for the medical images and/or multivalued images. For example, by rotating a radius of a predetermined length by using a point near the shadow center as a reference point, image values of the shadow in the medical image and/or multivalued image are sequentially sampled on the loop, and various processing is carried out to determined, based on the sampled image values, whether or not the shadow is a nidus candidate shadow. When the sampled image values can be obtained on the loop, a representative value of the loop is compared with a reference value obtained in advance for the nidus shadow to distinguish the nidus shadow. In addition, the discrimination is carried out based on the correlation between loops. Because various kinds of discrimination processing is carried out, the contents of the processing is complicated.
As described above, there is no technique for efficiently extracting plural regions from image data all together.